Advancements in airborne and satellite laser scanning technology provide an opportunity to obtain more accurate information about target locations on the ground. In this regard, Light Detection and Ranging (“LiDAR”) is an optical remote scanning technology used to identify distances to remote targets. For example, a laser pulse may be transmitted from a source location, such as an aircraft or satellite, to a target location on the ground. The distance to the target location may be quantified by measuring the time delay between transmission of the pulse and receipt of one or more reflected return signals. Moreover, the intensity of a reflected return signal may provide information about the attributes of the target. In this regard, targets on the ground will reflect return signals with varying amounts of intensity that depends on a number of different factors.
LiDAR optical remote scanning technology has aspects that make it well-suited for identifying attributes of target locations. For example, the wavelengths of a LiDAR laser pulse are typically produced in the ultraviolet, visible, or near infrared areas of the electromagnetic spectrum. These short wavelengths are very accurate in identifying the geographic location of targets that generate a return signal, such as vegetation. Also, LiDAR offers the ability to perform high sampling intensity, extensive aerial coverage, as well as the ability to penetrate the top layer of a vegetation canopy.
Those skilled in the art and others will recognize that LiDAR optical remote scanning technology may be used to obtain a sample set of information about targets on the ground. Typically LiDAR pulses are transmitted from a source location over a regularly spaced pattern. Thus, LiDAR technology may only be used to obtain definitive elevation information about a sample set of ground locations that are along the regularly spaced pattern. It would be beneficial to have a system that is capable of processing LiDAR data and accurately estimating the elevation of ground locations that are not contacted with a LiDAR laser pulse or other attribute that was not directly measured by LiDAR instrumentation.
Some existing systems use a technique known as spatial interpolation to predict the value of an attribute at an unknown point, such as the point's elevation, based on one or more sample point values. Typically, when applying spatial interpolation techniques to perform geographic estimates, a transformation is performed between information measured at scattered points to grids that are suitable for modeling and visualization. Using a grid, the elevation of a grid element may be predicted based on sample point values. In this regard, spatial interpolation is used to estimate a value of a variable at an unsampled location from data obtained from spatially related locations. Spatial interpolation is based on the principal of spatial dependence which measures the degree of dependence between near and distant points. However, transforming sample data onto a grid when performing spatial interpolation is algorithmically complex and a resource intensive task. In this regard, a long-standing need exists to perform spatial interpolation in a way that minimizes the performance impact of estimating attributes at a geographic point based on one or more sample point values.